Document Detail

Computerized detection of pulmonary nodules in computed tomography images.
MedLine Citation:
PMID:  8034453     Owner:  NLM     Status:  MEDLINE    
RATIONALE AND OBJECTIVES: Interpretation of computed tomographic (CT) scans of the lungs is a time-consuming task that involves visual correlation of possible nodules in one section with those in contiguous sections to distinguish actual nodules from blood vessels. Thus, the authors are developing automated methods to detect nodules on CT images of the thorax. METHODS: The computerized technique uses various computer-vision techniques and a priori information of the morphologic characteristics of pulmonary nodules. In each section, the external thoracic wall and lung boundaries are detected, and the features within the lung boundaries are subjected to gray-level thresholding operations. By analyzing the relationships between features arising at different threshold levels with respect to their shape, size, and location, each feature is assigned a likelihood of being a nodule or a vessel. Features in adjacent sections are compared to resolve ambiguous features. Detected nodule candidates are displayed in three dimensions within the lung. RESULTS: The system provided a sensitivity of 94% for nodule detection and an average of 1.25 false-positive results per case. CONCLUSIONS: Continued development of an automated method for detecting pulmonary nodules in CT scans is expected to aid radiologists in the task of locating nodules in three dimensions.
M L Giger; K T Bae; H MacMahon
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Investigative radiology     Volume:  29     ISSN:  0020-9996     ISO Abbreviation:  Invest Radiol     Publication Date:  1994 Apr 
Date Detail:
Created Date:  1994-08-16     Completed Date:  1994-08-16     Revised Date:  2010-03-24    
Medline Journal Info:
Nlm Unique ID:  0045377     Medline TA:  Invest Radiol     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  459-65     Citation Subset:  IM    
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637.
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MeSH Terms
Artificial Intelligence*
False Positive Reactions
Image Processing, Computer-Assisted*
Sensitivity and Specificity
Solitary Pulmonary Nodule / radiography*
Tomography, X-Ray Computed / methods*
Grant Support

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